The detection of underground voids is an area of growing importance within the mining, oil and gas, construction, and defense industries. For example, as with construction, the detection of old underground tunnels is necessary in areas where buildings are being built. Further to this point, the detection of water mains made of wood or concrete, as well as old collapsed basements or storage tanks is equally required during construction projects. In general, there is an ever-growing need for new and improved ways to detect underground tunnels of various sorts. Currently, beyond detections made visually at the ground site, underground tunnel detections are attempted by way of a variety of single sensor and multi-sensor approaches utilizing a broad spectrum of technologies. Some of the traditional technologies include seismic-acoustic methods utilizing compressional seismic (P) waves, electromagnetic and resistivity, ground penetrating radar, and magnetic methods. Some of the more recently developed approaches utilize microgravity and subsurface interface radar.
The seismic-acoustic methods typically utilize compressional seismic (P) waves propagating between sources and sensors positioned within vertical boreholes at depths near suspected tunnel depths. Likewise, electromagnetic methods utilize electromagnetic (EM) waves propagating between sources and sensors positioned within vertical boreholes at depths near suspected tunnel depths. The added requirement for boreholes only adds to the time and expense required to get data for interpretation. Still further, ground penetrating radar, subsurface interface radar and gravity related technical approaches may be somewhat advantageous since they may alleviate the need for boreholes. However, all of the current technologies being utilized toady in the mining, oil and gas, and defense industries for detecting underground tunnels contain various inherent problems such as excessive clutter, excessive signal loss due to varying soil/rock mediums, and excessive false positives and false negatives due to the inhomogeneities present underground. These inherent problems all serve to complicate and prevent reliable tunnel detection in the current mining, oil and gas, and defense industries.
The outputs of these various technologies typically provide sensed data in the form of a measured signal or an image representative of reflections and variations caused by the soil being scanned/sensed but do not actually provide an image of the tunnel itself. Typically, such output data first has to be interpreted through visual inspection by highly trained analysts before any determinations are formed. The interpretations are generally very subjective and highly unreliable. Processing rates tend to be very slow (measured in months) with an extremely high occurrence of false positives and false negatives. Hence, formal methods are still required to sort through the various noise and clutter so to accurately and reliably interpret the sensed data.
Accordingly, there exists a long felt need for an improved technology and method for detecting underground tunnels that alleviates the known inherent problems present within the technologies and methods currently being utilized today for tunnel detection in the various industries.